This course discusses (i) samples, populations, and the role of probability, (ii) mean, median, range and standard deviation, (iii) visual tools, (iv) sample space and event probabilities, addition & multiplication rules, permutation, and combination, (v) conditional probability and Bayes’ rule, (vi) random variables, discrete and continuous probability distributions, joint probability distributions, (vii) mathematical expectations and the theoretical definition of mean, variance, covariance, and correlation, (viii) normal distribution and its applications, (ix) sampling distributions and central limit theorem, (x) testing a statistical hypothesis, and (xi) simple linear regression and analysis of variance.
This course is organized with a focus on basic concepts and methods required during the feasibility phase of investment decisions, with the uncertainty dimension emphasized with a risk approach. Topics covered include financial statements, time value of money, identification of project cash flows, investment criteria, sensitivity and scenario analysis, and real option models. Students who pass this course satisfactorily learn how to (i) analyze financial statements, (ii) make revenue and operating and capital cost estimates, (iii) use basic investment decision criteria, (iv) account for risk and uncertainty in making investment decisions, and (v) evaluate risk at the decision-making stage.
The aim of this course is to provide the learner with the necessary tools to deal with the theory and practice of financial engineering. The learner is introduced to the mechanics and characteristics of futures/forwards, swaps, and options, learns the baseline valuation methods for these instruments, and studies the possible trading strategies that can be engineered through the use of these securities. Upon successful completion of the course, the learner should be able to describe the characteristics and potential uses of different types of derivatives, apply baseline valuation methods to determine their theoretical prices, and use these instruments in basic financial engineering applications.
This course introduces alternative forecasting methods and analyzes their use in decision making and designing a forecasting system. Following a brief introduction to the basics of forecasting, the course provides a review of core statistical concepts and data patterns. It then proceeds to introduce moving average & exponential smoothing methods, time series decomposition, and simple/multiple regression analysis and ARIMA models. Students who pass this course satisfactorily learn how to classify/analyze data, how to choose the best forecasting method based on context and available data, how to apply different forecasting models, and how to evaluate forecasting performance. Minitab and/or Excel applications of these concepts are also provided throughout the course.
This course focuses on economic analysis for engineering decision making. The topics covered include (i) the finance function in an industrial enterprise and reporting of accounting data, (ii) basic interest rate formulas, (ii) time value of money and present value analysis, (iii) investment decision criteria including NPV, IRR, and ERR, (iv) concepts of cost engineering, (v) project selection with multiple alternatives, uneven economic lives, equipment replacement, and (vi) a brief introduction to decision analysis.
The aim of the course is to provide the students the fundamentals of engineering statistics such as random sampling, methods of inference and comparative study. Upon successful completion of this course, students will be able to (i) analyze data using numerical summaries and visual displays, (ii) compute probabilities using sampling distributions, (iii) evaluate statistical inferences in engineering problems using confidence intervals and via formal hypothesis testing methods.
This course aims at providing students with the analytical tools and skills necessary for making efficient financial decisions. The course shoots off with time value of money and the associated DCF techniques. It then looks at how firms finance their projects and the types and characteristics of available financing instruments. DCF analysis is applied to the valuation of stocks and bonds. The relation between risk and return and the CAPM is covered next, along with the computation of expected returns, variances, and covariances for individual assets and portfolios. The NPV rule and other investment criteria for making capital budgeting decisions are introduced and combined with DCF techniques to develop intuitions instrumental in analyzing and evaluating real projects. WACC is introduced as a viable proxy for the discount rate that is applied to project cash flows. The course adjourns with a discussion of corporate capital structure and dividend policies.
This course aims at developing a structure for thinking about the theory of investment and addressing real-life investment problems in a systematic and scientific manner. The topics covered include portfolio selection via the Markowitz mean-variance optimization framework, optimal capital allocation across risky and risk-free investments, active portfolio management including the Black-Litterman method, and performance attribution. The coruse does not cover the art of stock picking nor does it provide foolproof ways to make money in financial markets. A number of important topics such as derivatives, taxes, and financial statements are covered in passing, leaving details to electives directed towards these issues.
This course covers the market mechanics and the pricing of different types of financial derivatives trading in derivative markets, and introduces the students to the role these securities play in the management of various types of risk borne by financial market participants. The lecture starts with futures, forwards, and swaps on stocks, bonds, interest rates, currencies, and commodities, and adjourns with mechanics of option contracts, option-based strategies, and the pricing of options. Upon the completion of this course, the students are expected to develop a working knowledge of how derivative instruments are priced and how they can be used for risk management purposes.
This course delivers a thorough treatment of the operations and risks of financial institutions. The lectures begin by introducing different types of institutions populating the financial landscape; move on to discuss the various sources of risk that affect the operations these institutions; and conclude by analyzing how their exposure to various risk sources can be identified, measured, and managed. Upon course completion, students are expected to develop a working knowledge of how financial institutions serve market participants and the risk management issues surrounding their operations.
This course aims at applying key concepts in the portfolio theory to real life engineering investment decisions. The goal is to arm the learner with a structure for thinking about how portfolio optimization can help them address real-life decisions under uncertainty in a systematic and scientific manner. The nature of uncertainty and possible criteria that a decision maker can use to deal with it is discussed. An examination of current academic work and its implications for decision making is also provided.
This course introduces the learner how financial derivatives can be used in the management of various types of operational and financial risk. The lecture starts by introducing futures, forwards, and swaps on various types of assets and adjourns with mechanics and pricing of option contracts. Upon successful completion of this course, the learner is expected to develop a working knowledge of how financial derivatives are traded and valued priced and how they can be used for risk management purposes.
This course serves as an introduction to the fundamental pillars of contemporary asset pricing theory. The discussions use the consumption based asset pricing model and the stochastic discount factor as a unifying framework within which the valuation of both real and financial assets can be analyzed. The course begins by introducing the basic toolkit of the asset pricing theory, and proceeds by utilizing this toolkit to interpret the established facts and the current advances in the asset pricing literature. The participants also get acquainted with popular asset pricing databases and the statistical tools that can be used to extract, process, and analyze the data from these databases. Each participant is expected to write a research proposal on how an asset pricing idea of their own choice can be tested and carry out the first few steps of the tests outlined in this research proposal.
This course specializes in two specific areas of finance: Asset Pricing and Corporate Finance. In the Asset Pricing section, academic work related to capital asset pricing models including CAPM, CCAPM and ICAPM, empirical tests of these models, and anomalies which led the way to the proliferation of multifactor models will be discussed. A particular emphasis will be given on hedge funds and mutual funds. In the Corporate Finance section, papers related to principal-agent problem, dividend and capital structure policy decisions, corporate governance, and executive compensation will be discussed.